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ISSN : 1738-3110
Optimal Forecasting for Sales at Convenience Stores in Korea Using a Seasonal ARIMA-Intervention Model
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Abstract
Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.
- keywords
- Convenience Stores, ARIMA-Intervention Model, Exponential Smoothing Method, Forecast
Reference
Anderson, T. W. (1971). The statistical analysis of time series. New York: Wiley.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: forecasting and control 3rd ed. Englewood Cliffs, NJ: Prentice Hall.
Box, G. E. P., & Tiao, G. C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of American Statistical Association, 70, 7079.
Brown, R. G. (1963). Smoothing, forecasting and prediction of discrete time series. Englewood Cliffs, NJ: Prentice Hall.
Choi, Byoung-Seon (1992). Univariate time series analysis I. Seoul, Korea: Sae Kyung Sa.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431.
Fuller, W. A. (1976). Introduction to statistical time series. New York: John Wiley & Sons, Inc.
Gardner, E. S. Jr (1985). Exponential smoothing: the state of the art. Journal of Forecasting, 4, 1–28.
Gardner, E. S. Jr (2006). Exponential smoothing: The state of the art Part II. International Journal of Forecasting, 22, 637–677.
Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.
Han, San-Ho, Yang, Heo-Chang, & Kim, Jong-Lak (2015). The impact of service quality on service satisfaction and store loyalty: Service value as a moderator. Journal of Distribution Science, 13(10), 101-108.
Han, Yoo-Nah, & Kim Dong-Ho (2015). Creating profits with nonunion workers: A cases study of market basket. The East Asian Journal of Business Management, 5(1), 37-41.
Jeong, Dong-Bin (2010). Demanding forecasting of time series I. Seoul, Korea: Hannarae Academy.
Jeong, Dong-Bin (2011). Demanding forecasting of time series I. Seoul, Korea: Hannarae Academy.
Jeong, Dong-Bin (2015). Eviews solution for time series analyst. Seoul, Korea: Hwangsoogirlum Academy.
Jeong, Dong-Bin (2016). A note on association for Korean markets using correspondence analysis. The International Journal of Industrial Distribution & Business, 7(3), 5-13.
Kim, Jong-Lak, Lee, Young-Chul, Han, Sang-Ho, & Lim, Su-Ji (2013). The impact of service quality on customer satisfaction, service value, and store loyalty in a university-based convenience store. Journal of Distribution Science, 11(5), 5-15.
Korean Statistical Information Service (2016). 2016 survey of service trend of Korean statistics administration. Daejeon, Korea: Retrieved August 31, 2016, from http://kosis.kr/statHtml/statHtml.do?orgId=101& tblId=DT_1K21003&vw_cd=MT_ZTITLE&list_id=JG1& seqNo=&lang_mode=ko&language=kor&obj_var_id= &itm_id=&conn_path=E1)
Kwon, Oh-Cheul, Kim, Pan-Jun, Kim, Hong-Seop, Park, Yeung-Kurn, Park, Hyung-Jin, Lee, Jang-Hwan, Kim, Yoo-Oh, Kim, Jin-Seong, Kim, Pan-Jin, NamKung, Suk, Park, Chul-Ju, Youn, Myung-Kil, & Hwang-Hwa-Cheol (2007). A note on the unification on retail trade terminology. Journal of Distribution Science, 5(2), 5-16.
Ljung, G. M. & Box, G. E. P. (1978). On a measure of lack of Fit in time series models. Biometrika, 65, 297303.
Pankratz, A. (1983). Forecasting with univariate Box- Jenkins models: Concepts and cases. New York: John Wiley & Sons, Inc.
Pankratz, A. (1991). Forecasting with dynamic regression models. New York: John Wiley & Sons, Inc.
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335- 346.
Song, June-Mo (2016). A study on demand forecasting for Jeju-bound tourists by travel purpose using seasonal ARIMA-Intervention model. Journal of the Korean Data & Information Science Society, 27(3), 725-732.
Tsay, R. S., & Tiao, G. C. (1984). Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and non- stationary ARMA Models. Journal of the American Statistical Association, 79, 84-96.
Trigg, D. W., & Leach, D. H. (1967). Exponential smoothing with an adaptive response rate. Operational Research Quarterly, 18, 5359.
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